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# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/ppca.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.script_building.dag import OutputType
from systemds.utils.consts import VALID_INPUT_TYPES


def ppca(X: Matrix,
         **kwargs: Dict[str, VALID_INPUT_TYPES]):
    """
     This script performs Probabilistic Principal Component Analysis (PCA) on the given input data.
     It is based on paper: sPCA: Scalable Principal Component Analysis for Big Data on Distributed
     Platforms. Tarek Elgamal et.al.
    
    
    
    :param X: n x m input feature matrix
    :param k: indicates dimension of the new vector space constructed from eigen vectors
    :param maxi: maximum number of iterations until convergence
    :param tolobj: objective function tolerance value to stop ppca algorithm
    :param tolrecerr: reconstruction error tolerance value to stop the algorithm
    :param verbose: verbose debug output
    :return: Output feature matrix with K columns
    :return: Output dominant eigen vectors (can be used for projections)
    """

    params_dict = {'X': X}
    params_dict.update(kwargs)
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    output_nodes = [vX_0, vX_1, ]

    op = MultiReturn(X.sds_context, 'ppca', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]

    return op
